Linked Life Data

12935332

Source:http://linkedlifedata.com/resource/pubmed/id/12935332

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3-4
pubmed:dateCreated
2003-8-25
pubmed:abstractText
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve. We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters. We show that unobserved time points can be reconstructed using our method with 10-15% less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression profiles, and we demonstrate that this is particularly effective when applied to nonuniformly sampled data. Our continuous alignment algorithm also avoids difficulties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the specification of parameterized functions, which helps to avoid overfitting. We demonstrate that our algorithm produces stable low-error alignments on real expression data and further show a specific application to yeast knock-out data that produces biologically meaningful results.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1066-5277
pubmed:author
pubmed:issnType
Print
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
341-56
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Continuous representations of time-series gene expression data.
pubmed:affiliation
MIT Laboratory for Computer Science, 200 Technology Square, Cambridge, MA 02139, USA. zivbj@mit.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't